作者
Kaifeng Bi, Lingxi Xie, Hengheng Zhang, Xin Chen, Xiaotao Gu, Qi Tian
发表日期
2023/7/20
期刊
Nature
卷号
619
期号
7970
页码范围
533-538
出版商
Nature Publishing Group UK
简介
Weather forecasting is important for science and society. At present, the most accurate forecast system is the numerical weather prediction (NWP) method, which represents atmospheric states as discretized grids and numerically solves partial differential equations that describe the transition between those states. However, this procedure is computationally expensive. Recently, artificial-intelligence-based methods have shown potential in accelerating weather forecasting by orders of magnitude, but the forecast accuracy is still significantly lower than that of NWP methods. Here we introduce an artificial-intelligence-based method for accurate, medium-range global weather forecasting. We show that three-dimensional deep networks equipped with Earth-specific priors are effective at dealing with complex patterns in weather data, and that a hierarchical temporal aggregation strategy reduces accumulation errors in …
学术搜索中的文章
K Bi, L Xie, H Zhang, X Chen, X Gu, Q Tian - Nature, 2023
K Bi, L Xie, H Zhang, X Chen, X Gu, Q Tian - arXiv preprint arXiv:2211.02556, 2022
Pangu-weather: A 3d high-resolution model for fast and accurate global weather forecast. arXiv 2022*
K Bi, L Xie, H Zhang, X Chen, X Gu, Q Tian - arXiv preprint arXiv:2211.02556, 2022
K Bi, L Xie, H Zhang, X Chen, X Gu, Q Tian - 2022
K Bi, L Xie, H Zhang, X Chen, X Gu, Q Tian - URL https://arxiv. org/abs/2211.02556
K Bi, L Xie, H Zhang, X Chen, X Gu, Q Tian - arXiv preprint arXiv:2211.02556
K Bi, L Xie, H Zhang, X Chen, X Gu, Q Tian - arXiv preprint arXiv:2211.02556, 2022
K Bi, L Xie, H Zhang, X Chen, X Gu, Q Tian - Nature, 2023